Leveraging active learning for relevance feedback using an information theoretic diversity measure

Authors:
Charlie K. Dagli;Shyamsundar Rajaram;Thomas S. Huang
Affiliations:
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL;Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL;Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Year:
2006

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Abstract

Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user's effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues by proposing a fundamentally motivated, information-theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Comparative testing and results are reported and thoughts for future work are presented.